{"doi":"10.1101/2024.03.07.583975","title":"k-Means NANI: an improved clustering algorithm for Molecular Dynamics simulations","abstract":"Abstract One of the key challenges of k -means clustering is the seed selection or the initial centroid estimation since the clustering result depends heavily on this choice. Alternatives such as k -means++ have mitigated this limitation by estimating the centroids using an empirical probability distribution. However, with high-dimensional and complex datasets such as those obtained from molecular simulation, k -means++ fails to partition the data in an optimal manner. Furthermore, stochastic elements in all flavors of k -means++ will lead to a lack of reproducibility. K -means N -Ary Natural Initiation (NANI) is presented as an alternative to tackle this challenge by using efficient n -ary comparisons to both identify high-density regions in the data and select a diverse set of initial conformations. Centroids generated from NANI are not only representative of the data and different from one another, helping k -means to partition the data accurately, but also deterministic, providing consistent cluster populations across replicates. From peptide and protein folding molecular simulations, NANI was able to create compact and well-separated clusters as well as accurately find the metastable states that agree with the literature. NANI can cluster diverse datasets and be used as a standalone tool or as part of our MDANCE clustering package.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2024,"id":486778,"datarank":0.20794415416798362,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"self_citation_contribution":0.20794415416798362,"citation_network_contribution":0.0,"self_endowment_contribution":0.20794415416798362,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9553,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":7202,"name":"Daniel R. Roe","orcid":"0000-0002-5834-2447","position":1,"is_corresponding":false},{"id":1209647,"name":"Matthew Kochert","orcid":"0000-0003-1997-2128","position":2,"is_corresponding":false},{"id":39812,"name":"Carlos Simmerling","orcid":"0000-0002-7252-4730","position":3,"is_corresponding":false},{"id":1167935,"name":"Ramón Alain Miranda‐Quintana","orcid":"0000-0003-2121-4449","position":4,"is_corresponding":false},{"id":1170670,"name":"Lexin Chen","orcid":"0000-0002-9528-942X","position":0,"is_corresponding":true}],"reference_count":59,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-19T02:08:01.404471Z","pmid":"38496504","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}